Spread spectrum software receiver

ABSTRACT

A spread spectrum signal receiver comprises a radio signal processing unit, at least partly implemented in software running on a processor. The processing unit determines a candidate collection of subsets of signal sources from a group of potential sources, by the criterion that an anticipated processing intensity required to derive position/time related data from that subset is equal to or less than a maximum specified processing intensity. Each subset contains at least a minimum number of signal sources necessary to produce position/time related data. The processing unit also selects a set of preferred signal sources from a candidate subset, based on a highest estimated quality of the position/time related data attainable during a subsequent operating period for the receiver without exceeding the maximum specified processing intensity. During the subsequent operating period, the receiver receives spread spectrum signals from the selected set of signal sources and produces position/time related data therefrom.

THE BACKGROUND OF THE INVENTION AND PRIOR ART

The present invention relates generally to reception and processing of spread spectrum signals. More particularly the invention relates to a spread spectrum receiver according to the preamble of claim 1 and a method according to the preamble of claim 11. The invention also relates to a computer program product according to claim 21 and a computer readable medium according to claim 22.

Spread spectrum transmission solutions are becoming increasingly important, for instance in global navigation satellite systems (GNSS). Presently, the Global Positioning System (GPS; U.S. Government) is the dominant system, however alternative systems are expected to gain increased importance in the future. So far, the Global Orbiting Navigation Satellite System (GLONASS; Russian Federation Ministry of Defense) and the Galileo system (the European programme for global navigation services) constitute the major alternative GNSSs. Various systems also exist for enhancing the coverage, the availability and/or the quality of at least one GNSS in a specific region. The Quasi-Zenith Satellite System (QZSS; Advanced Space Business Corporation in Japan), the Wide Area Augmentation System (WAAS; The U.S. Federal Aviation Administration and the Department of Transportation) and the European Geostationary Navigation Overlay Service (EGNOS; a joint project of the European Space Agency, the European Commission and Eurocontrol—the European Organisation for the Safety of Air Navigation) represent examples of such augmentation systems for GPS, and in the latter case GPS and GLONASS.

Unfortunately, the dissimilarities in the frequency bands, and especially the signal formats used in the different systems, result in the situation that a signal receiver adapted for one system is generally not able to receive and process signals from sources belonging to a different system. Thus, multiple receiver chains, or one receiver chain with plural signal paths, are required to enable reception of signals from more than one type of system. Including more than one receiver chain in a single device renders the device expensive, bulky and/or heavy. Therefore, a programmable software receiver solution is desired, which enables processing of many signal formats in one processor, e.g. a CPU (central processing unit) or a DSP (digital signal processor). Namely, in such a design, it is possible to adapt the signal processing principles to a plurality of signal formats. A software-based GNSS receiver is also advantageous in that this kind of receiver may co-exist efficiently with other types of signal receivers, signal processing devices and/or software applications, for example in a laptop computer, a mobile telephone, or a PDA (Personal Digital Assistant).

However, software receiver implementations are associated with one important drawback in comparison with corresponding hardware designs. Namely, a software implementation running on a generic microprocessor is typically less energy efficient (in terms of energy or power per output data) than one running on a dedicated hardware implementation, e.g. represented by an ASIC (Application Specific Integrated Circuit).

For reasons mentioned above, it is advantageous to integrate a software-based receiver into a portable/handheld device, provided such integration addresses the additional challenge of these devices' typically limited battery capacity. Thus, it is important to optimize the use of the power resources as well. With these things in mind, we will now briefly discuss the prior art in this area.

U.S. Pat. No. 6,710,578 discloses a method for power resource management in a hardware-based portable communication device, such as a radiotelephone. The device may be operated in a plurality of operational modes, and before entry into a particular user-selected mode, an estimate of the available power is calculated. It is then predicted whether these resources are sufficient for the selected mode, and if it is estimated that the resources are insufficient, the operation of the device is restricted with respect to at least one operational mode. However, the selection of the signal sources being used is not influenced by these measurements. Furthermore, the solution is entirely focused on a hardware implementation.

U.S. Pat. No. 6,727,850 describes a method and a receiver apparatus for selecting optimal satellites to locate an object. A satellite list including the coordinates of the visible satellites is generated. Then, the satellites having the highest redundancy are eliminated from the list, such that an intended number of satellites remain on the list. Thereby, a required computation volume to produce the list becomes relatively low. Here, the redundancy is defined by a degree of overlap that a satellite has with other satellites on the list. However, besides this redundancy measure, no quality-related parameter influences the choice of satellites used by the receiver. Furthermore, the design presumes a hardware implementation.

The published U.S. patent application 2005/0140545 reveals a GPS receiver having a software-implemented correlator, which is adapted to render possible seamless integration of multiple technologies without any compromise in performance levels and without the need for customized hardware. Allegedly, the design also reduces the power consumption as a result of fewer hardware components and the ability to change the sampling frequency. Nevertheless, optimization of power resources is not used in any way in the space vehicle (SV) selection algorithm.

SUMMARY OF THE INVENTION

The object of the present invention is therefore to alleviate the above problems and to provide a highly power-efficient software-based solution for receiving and processing spread spectrum signals.

According to the invention, the object is achieved by the receiver as initially described, wherein the processing unit is adapted to determine a candidate collection of subsets of signal sources among a group of potential signal sources. Each member of the collection is a subset of the group of potential signal sources defined by a criterion that an anticipated processing intensity required to derive the position/time related data from that subset is equal to or less than a maximum specified processing intensity. The processing unit is further adapted to select the set of preferred signal sources based on a candidate subset, which is associated with a highest estimated quality of the position/time related data attainable during a subsequent operating period for the receiver without the adjusted allocation exceeding the maximum specified processing intensity.

One important advantage of this design is that the receiver's energy consumption can be made very low, or at least controlled below a predefined threshold, while maintaining a good quality of the position/time related data. Moreover, it is possible to configure the receiver such that if additional processing capacity becomes available, the processing demand may be allowed to increase by a certain amount, if this is estimated to improve the quality of the position/time related data. Naturally, the candidate collection of subsets need not include all theoretically possible constellations of the potential signal sources. For example, subsets that prima facie represent highly unfavorable, or even non-working combinations, may be discarded directly.

According to one preferred embodiment of the invention, it is presumed that at least two of the signal sources in the group of potential signal sources emit signals of mutually different signal formats. Therefore, the processing unit is adapted to estimate the anticipated processing intensity by considering a respective computational complexity for processing signals of each of said formats. Thus, a typical computational intensity necessary to process the signals of each format (e.g. GPS and Galileo respectively) may be weighed into the processing-demand estimation.

According to a further preferred embodiment of the invention, the processing unit is adapted to estimate the anticipated processing intensity by considering a predetermined approximation of a processing intensity required to derive the position/time related data from a particular signal source based on a format of the signals of emitted from the source, and/or a parameter, which reflects the source's elevation angle relative to the receiver. Thereby, an initial estimate of the anticipated processing intensity can be obtained in a very straightforward manner.

According to yet another preferred embodiment of the invention, the processing unit is adapted to adjust a respective processing intensity allocated to process the signals from each signal source in the set of preferred signal sources. This adjustment is performed such that the maximum specified processing intensity is not exceeded. Thus, a larger fraction of the processing resources can be used to handle the signals of relatively poor quality while a smaller fraction is used to handle the high-quality signals. This type of reallocation may be useful for example when the relatively low signal quality is the result of a low elevation angle of the signal source.

According to still another preferred embodiment of the invention, the processing unit is adapted to estimate the anticipated processing intensity by considering a respective signal quality of each signal in the group of potential signal sources. Here, the signal quality is reflected by one or more of: a signal power parameter estimation, a noise density parameter estimation, a pseudorange error parameter estimation, a parameter indicating detected interference, and signal source health/status data specifying whether or not a specific signal source is presently available for use. The signal power parameter estimation and the noise density parameter estimation may, in turn, be used to estimate a signal-to-noise ratio. The pseudorange error parameter estimation, in turn, may include a multipath distortion parameter estimation; a parameter indicating estimated atmospheric delays (ionospheric and/or tropospheric) and/or a parameter indicating satellite-based signal distortions (i.e. signal non-idealities originating from the satellite). Any detected interference detection may be broken up into narrowband interference, wideband interference, jamming and/or spoofing. The results of these tests and estimates are combined to yield a determination of the feasibility of acquiring or tracking a particular signal, and an estimate of the processing intensity required. Thus, a reliable basis is established for assessing the processing demand.

According to yet another preferred embodiment of the invention, the processing unit is adapted to estimate the anticipated processing intensity by considering at least one geometry parameter. This parameter, in turn, reflects a spatial position of each of the signal sources in the group of potential signal sources relative to a current position/time for the receiver. For example, the so-called dilution of precision (DOP) concept may be used to encapsulate said geometry parameters. These parameters, combined with a measured or assumed set of errors for the individual signal sources, produce an estimate of the accuracy of a position fix. Since each signal source is associated with an anticipated computational load, the at least one geometry parameter provides a measure of which accuracy that can be obtained at the cost of a particular processing intensity.

According to another preferred embodiment of the invention, the processing unit is adapted to estimate the anticipated processing intensity by considering whether a future operational mode of the receiver during the subsequent operating period is expected to be identical to a current operational mode of the receiver. Preferably, if the future operational mode is different from the current operational mode, the processing unit is adapted to estimate a respective anticipated processing intensity required to derive the position/time related data from each of the subsets in the candidate collection of subsets in the future operational mode. Hence, an accurate estimate can be made of the processing demand.

According to yet another preferred embodiment of the invention, each signal source is represented by a particular satellite of at least one global navigation satellite system. The group of potential signal sources here represents all satellites of the at least one global navigation satellite system, which are visible from a current position/time for the receiver. For instance, the group of potential signal sources may be determined from so-called almanac and/or ephemeris data describing the satellites' movements over time. Moreover, various types of assisted GNSS solutions may provide equivalent or additional aiding information.

According to still another preferred embodiment of the invention, the processing unit is adapted to optimize a cost function expressing a processing intensity as a function of a data quality level with respect to the position/time related data. This function is used to determine the candidate subset that is associated with the highest estimated quality of the position/time related data during the subsequent operating period under the condition given by the maximum specified processing intensity. Hence, an optimal candidate set can be determined efficiently, for example by means of linear programming.

According to another aspect of the invention the object is achieved by the method described initially, involving the steps of: determining a candidate collection of subsets of signal sources among a group of potential signal sources, each member of the collection being a subset of the group of potential signal sources defined by a criterion that an anticipated processing intensity required to derive the position/time related data from that subset is equal to or less than a maximum specified processing intensity; and

selecting the set of preferred signal sources based on a candidate subset being associated with a highest estimated quality of the position/time related data attainable during a subsequent operating period for the receiver without exceeding the maximum specified processing intensity.

The advantages of this method, as well as the preferred embodiments thereof, are apparent from the discussion above with reference to the proposed receiver.

According to a further aspect of the invention the object is achieved by a computer program product, which is directly loadable into the memory of a computer, and includes software for controlling the method proposed above when said program is run on a computer.

According to another aspect of the invention the object is achieved by a computer readable medium, having a program recorded thereon, where the program is to control a computer to perform the method proposed above.

Further advantages, beneficial features and applications of the present invention will be apparent from the following description and the dependent claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is now to be explained more closely by means of preferred embodiments, which are disclosed as examples, and with reference to the attached drawings.

FIG. 1 shows a block diagram of a spread spectrum signal receiver according to one embodiment of the invention;

FIG. 2 illustrates how signal sources in the form of the satellites of a number of GNSSs orbit the Earth;

FIG. 3 illustrates a group of potential signal sources presently visible from a particular receiver position;

FIG. 4 illustrates how candidate subsets of signal sources may be defined in a group of potential signal sources according to one embodiment of the invention; and

FIG. 5 illustrates, by means of a flow diagram, the general method of operating a spread spectrum receiver according to the invention.

DESCRIPTION OF PREFERRED EMBODIMENTS OF THE INVENTION

FIG. 1 shows a block diagram of a spread spectrum receiver 100 according to one embodiment of the invention. The receiver 100 includes a radio front-end unit 110, an interface unit 120 and a radio signal processing unit 135. The receiver 100 preferably also includes a computer readable medium 140, such as a memory buffer, storing a program which is adapted to control the processing unit 135 to operate according to the proposed principle.

The radio front-end unit 110 has an antenna means 105 adapted to receive radio signals S_(HF) from a plurality of signal sources, for example a set of satellites belonging to one or more GNSSs. To this aim the antenna means is designed to receive radio frequency signals in at least one frequency band, e.g. the L1-, L2- and/or L5-bands, i.e. having spectra ranging from 1563 MHz to 1587 MHz, 1215 MHz to 1240 MHz and 1155 MHz to 1197 MHz respectively. Furthermore, the radio front-end unit 110 is adapted to perform sampling and digitizing of the received radio signals S_(HF), and to produce a resulting digital representation S_(BP), based on which the radio signal processing unit 135 can perform relevant further signal processing to generate position/time related data D_(PT). For example, the unit 110 may directly sample a bandpass version of the radio signals S_(HF), or the unit 110 may execute I/Q bandpass sampling, and thus frequency down-convert the received signals S_(HF) to the baseband.

The interface unit 120 is adapted to interconnect the radio front-end unit 110 with the radio signal processing unit 135 by converting the digital representation S_(BP) from the radio front-end unit 110 to a data format d_(F) being suitable for handling by the radio signal processing unit 135.

The radio signal processing unit 135 is at least partly implemented in software 135 s running on a processor 130. Preferably, the radio signal processing unit 135 is entirely implemented in the software 135 s. However it is feasible that one or more separate units, e.g. realized in an FPGA (Field Programmable Gate Array) design or an ASIC, are adapted to perform at least one of the unit's 135 processing functions.

According to the invention, the processing unit 135 is adapted to select a set of preferred signal sources from which the spread spectrum signals S_(HF) are received to form a basis for the position/time related data D_(PT). To this aim, the processing unit 135 is adapted to determine a candidate collection of subsets of signal sources among a group of potential signal sources. Each member of the collection is a subset of the group of potential signal sources, which is defined by a criterion that an anticipated processing intensity required to derive the position/time related data D_(PT) from that subset is equal to or less than a maximum specified processing intensity. Naturally, the candidate subsets need not include all theoretically possible constellations of the potential signal sources. Preferably, any subsets that can immediately be deduced to represent highly unfavorable, or even non-working combinations, are discarded directly (i.e. without any actual determination of the anticipated processing intensity required to derive the position/time related data D_(PT) based on the signals from these subsets of signal sources). The processing unit 135 is adapted to select the set of preferred signal sources based on a candidate subset, which is associated with a highest estimated quality of the position/time related data D_(PT) attainable without exceeding the maximum specified processing intensity during a subsequent operating period for the receiver 100. In other words, the set of preferred signal sources is defined as the candidate subset which is estimated to generate the data D_(PT) of the best possible quality for a particular amount of invested processing. Further details regarding the proposed selection of signal sources will be discussed below with reference to FIGS. 3 and 4.

According to one preferred embodiment of the invention, we presume that at least two of the signal sources in the group of potential signal sources emit signals of mutually different signal formats. For example, a number of signal sources may be represented by Galileo satellites, and at least one signal source may be represented by a non-Galileo satellite, which thus may be a GPS, a QZSS satellite, or a geostationary signal source in the form of an EGNOS satellite.

Here, the processing unit 135 is adapted to estimate the anticipated processing intensity by considering a respective computational complexity for processing signals of each of said formats, i.e. in the above example, the Galileo format and the relevant other format(s), GPS, GLONASS and/or QZSS. The specifications of the GPS and Galileo formats are such that, provided identical physical circumstances, a GPS signal requires some-what less processing power in order to derive resulting position/time related data. In reality, however, a vast number of additional factors influence the estimated processing demand required to produce this data, such as a pseudorange error parameter estimation, a parameter indicating detected interference, signal source health/status (data specifying whether or not a specific signal source is presently available for use) and various geometry factors, which may be described in terms of DOP.

According to one preferred embodiment of the invention, the processing unit 135 is adapted to estimate the anticipated processing intensity by considering a predetermined approximation of a processing intensity required to derive the position/time related data D_(PT) from a particular signal source. This predetermined approximation is based on the format of the signals emitted from the source and/or a parameter reflecting the source's elevation angle relative to the receiver 100.

FIG. 2 schematically shows a number signal sources in the form of the satellites S1-S21 orbiting the Earth 200. The satellites may either all belong to a single GNSS, or they may belong to two or more different systems. For example, the satellites S1, S4, S7, S10, S13, S16 and S19 may be GPS satellites; while the satellites S2, S5, S8, S11, S14, S17 and S20 are Galileo satellites; and the satellites S3, S6, S9, S12, S15, S18 and S21 are GLONASS satellites. It is worth noting, however, that FIG. 2 is not completely representative because it is likely that the total number of GPS, GLONASS and Galileo satellites will be significantly larger than the number of satellites shown in this figure.

FIG. 3 illustrates a group of potential signal sources S1-S9 of the satellites shown in FIG. 2 that are visible from a particular receiver position at a particular instant, i.e. at a given position/time PT_(R). Here, a visibility limit H(PT_(R)) is schematically specified as the horizon seen from the position/time PT_(R). In reality, however, the receiver preferably applies an elevation mask angle, e.g. equivalent to 50 elevation above the horizon, i.a. to avoid large multipath errors.

As mentioned above, a tentative group of potential signal sources is preferably derived from an almanac in the receiver. This data describes the satellites' movements over time. Alternatively, or as a complement thereto, said group may be derived from broadcast ephemeris data and/or one or more assisted GNSS services available to the receiver. Normally, the ephemeris data is associated with an issue of data ephemeris (IODE) parameter, which indicates how old the information is. Depending on the time span since the most recent update of the receiver's position and/or time references, the expected composition of the tentative group and the expected positions of each member in said group may be imperfect or incorrect.

Generally, somewhat more processing power is required to derive the position/time related data based on Galileo signals than based on GPS signals. However, if the IODE indicates a much more recent update of the Galileo satellites than of the GPS satellites, it may still be beneficial to focus the selection of the preferred set of signal sources on the Galileo satellites because here the knowledge of the tentative group can be expected to be more accurate. Of course, a full acquisition procedure in which signals are registered from all the actually available signal sources provides the most accurate characterization of the group of potential signal sources S1-S9, and could in principle be used to identify which subsets require an anticipated processing intensity equal to or less than the maximum specified processing intensity to derive the position/time related data. Namely, one or more signal sources may malfunction, and/or the receiver may be located in a radio shadow with respect to one or more of the signal sources, and this kind of information is not derivable from the above-mentioned almanac or ephemeris data.

Nevertheless, provided the example GNSSs mentioned above, the group of potential signal sources S1-S9 includes three GPS satellites S2, S4 and S7, three Galileo satellites S2, S5 and S8 respective three GLONASS satellites S3, S6 and S9. Consequently, the signal sources in the group of potential signal sources S1-S9 emit signals of mutually different signal formats.

In principle, the processing unit 135 is adapted to estimate an anticipated processing intensity required to derive the position/time related data DP_(T) from each combination of the potential signal sources S1-S9. For each combination, the processing unit 135 is adapted to determine whether or not the position/time related data DP_(T) can be derived based on signals from a subset containing these signal sources without exceeding the maximum specified processing intensity. In practice, however, the processing unit 135 determines a more limited candidate collection of subsets of signal sources among a group of potential signal sources S1-S9. For example, the processing unit 135 may utilize predetermined approximations of the processing intensity required to derive the position/time related data D_(PT) from a particular signal source based on the format of the signals of emitted from the source and/or a parameter reflecting the source's elevation angle relative to the receiver 100.

FIG. 4 shows a relatively small number of candidate subsets CSS₁-CSS_(j), which may be defined from the group of potential signal sources S1-S9. In this example, for reasons of clarity, the candidate subsets CSS₁, CSS₂, CSS₃, CSS₄ and CSS_(j) contain relatively few signal sources (four or five). Specifically, the candidate subset CSS₁ contains the signal sources S1, S2, S3 and S4; the candidate subset CSS₂ contains the signal sources S2, S3, S4 and S5; the candidate subset CSS₃ contains the signal sources S4, S5, S6 and S7; the candidate subset CSS₄ contains the signal sources S5, S6, S7, S8 and S9; and the candidate subset CSS_(j) contains the signal sources S6, S7, S8 and S9. Normally, a set of four signal sources is the minimum number of signal sources necessary to produce a position estimate. Moreover, for reasons of clarity, the candidate subsets CSS₁-CSS_(j) are here defined such that each set contains a group of signal sources being positioned relatively proximate to one another. In an actual case, any geometrical interrelationships between the signal sources and the receiver are possible, provided that the signal sources lie within the visibility limit H(PT_(R)) from the receiver. Furthermore, according to the invention, each candidate subset CSS₁-CSS_(j) contains at least a predefined minimum number of signal sources necessary to produce the position/time related data D_(PT) in the current operational mode. For certain types of timing measurements as few as a single signal source may be sufficient. However, for highly accurate positioning, 8, 12, or even more signal sources may be needed. Particularly in such a case, it is advantageous to utilize the satellite signals from more than one GNSS. One example is a situation where the receiver is located in an “urban canyon.” The difference in position accuracy may not be large between a 7- and an 8-satellite solution. However, the robustness may be considerably improved if the receiver can track signals from more than one GNSS because then the potential number of visible signal sources becomes larger.

Based on the candidate subsets CSS₁-CSS_(j), a set of preferred signal sources is selected, such that the selected set, say CSS₃, is the candidate subset that is associated with a highest estimated quality of the position/time related data D_(PT) attainable without exceeding the maximum specified processing intensity. This estimation is assumed to be valid during a subsequent operating period for the receiver. The subsequent operating period is a future interval of any duration, for example represented by a number of processor clock cycles. In any case, the subsequent operating period may include any kind of receiver operation, such as acquisition, tracking or oscillator synchronization.

In order to attain an accurate estimate of the anticipated processing intensity, according one preferred embodiment of the invention, the processing unit 135 is adapted to determine a respective estimated signal quality of each signal in the group of potential signal sources S1-S9. The signal quality is here reflected by a signal power estimation and/or a noise density estimation. The two may be combined to compute an estimate of the received signal-to-noise ratio.

Alternatively, or as a complement thereto, the signal quality may be reflected by a pseudorange error parameter estimation. This estimation, in turn, may include estimation of a parameter expressing multipath distortion, parameters indicating estimated atmospheric delays (i.e. delays introduced by the ionosphere and/or in the troposphere) and/or a parameter indicating any satellite-based signal distortions (i.e. signal non-idealities caused by problems in the satellite).

Alternatively, or as yet another complement, the signal quality may be reflected by a parameter indicating interference detection. For example, narrowband interference may be detected (which typically is an amount of undesired sinusoidal energy), wideband interference may be detected (which typically is an amount of white or colored noise energy), jamming signals may be detected and/or spoofing signals may be detected, i.e. signals intentionally transmitted to disturb or deceive the receiver. According to one preferred embodiment of the invention, the interference parameter has the following influence on the selection of signal sources in the preferred set. If a particular frequency band is found to be subjected to narrowband interference or jamming and such interference or jamming is determined to render the ranging and/or positioning process unusable in that band, signal sources from alternative frequency bands, if available, are preferably selected. A determination can then be made about whether or not it is possible to meet the position/time data quality requirements using the available sources within the existing constraint of processing intensity. If, on the other hand, broadband interference or jamming is detected, the procedure involves calculating how much more averaging will be necessary to obtain ranges of the desired quality, or determining if no attainable amount of averaging will be sufficient. The estimates of the required processing demand are then revised accordingly.

Alternatively, or as still another complement, estimates of atmospheric delay, which may be represented by the so-called Klobuchar parameters transmitted by the signal sources, may also be factored in to the processing demand calculation.

Furthermore, the signal quality may be reflected by signal source health/status data indicating whether or not a specific signal source is presently available for use. Such data is likewise typically included in messages being repeatedly transmitted from the signal sources.

According to another preferred embodiment of the invention, the processing unit 135 is adapted to estimate the anticipated processing intensity by considering at least one geometry parameter, which reflects a spatial position of each of the signal sources in the group of potential signal sources S1-S9 relative to the receiver's current position/time PT_(R). Various forms of DOP measures constitute examples of such geometry parameters, e.g. GDOP (Geometric DOP), PDOP (Position DOP), VDOP (Vertical DOP), HDOP (Horizontal DOP) and TDOP (Time DOP). Since the DOP depends on the specific geometric relationships between the satellites and the receiver, and therefore varies over time, signals would preferably be received from those satellites which yield the best DOP, all other factors being equal. This is equivalent to optimizing the selection of satellites such that a lowest possible DOP value is obtained. Naturally, however, this optimization only pertains to the DOP measure as such. In an actual case, numerous factors besides DOP also influence the quality of the position/time related data D_(PT), and thus the processing demand for generating this data at a particular quality.

For efficient operation, according to one preferred embodiment of the invention, the processing unit 135 is adapted to estimate the candidate subset, say CSS₃ above, which is associated with the highest estimated quality of the position/time related data D_(PT) attainable without exceeding the maximum specified processing intensity by optimizing a cost function. This function expresses a processing intensity as a function of a data quality level with respect to the position/time related data D_(PT). For example, by applying linear programming (e.g. convex optimization), to the cost function, said candidate subset CSS₃ can be determined relatively quickly.

According to one preferred embodiment of the invention, the processing unit 135 is adapted to adjust a respective processing intensity allocated to process the signals from each signal source S4, S5, S6 and S7 in the set of preferred signal sources CSS₃. This adjustment is performed such that the maximum specified processing intensity is not exceeded. Thus, a larger fraction of the processing resources can be used to handle the signals of relatively poor quality while a smaller fraction is used to handle the high-quality signals. This type of reallocation may be useful when the relatively low signal quality is the result of a low elevation angle of the signal source.

Provided that the processor 130 has adequate processing resources, it may be preferable to hold one or more channels in the processing unit 135 open to repeatedly evaluate the quality of at least one ranging signal source, or to perform said evaluation on one or more active channels from time to time. Thereby, whenever a set of signals sources including the evaluated source(s) is estimated to provide a better data quality per invested processor cycle than the currently selected subset, the selection of signals sources can be modified to include one or more of the evaluated sources, either as a complement to, or as a substitute for, all or some of the previously selected sources.

According to one preferred embodiment of the invention, the processing unit 135 is adapted to estimate the anticipated processing intensity by considering whether or not a future operational mode of the receiver 100, i.e. during the subsequent operating period, is expected to be identical to a current operational mode of the receiver 100. The operational modes may include a first mode in which real-time navigation is provided (here, the receiver preferably produces position updates as often as technically possible); a second mode in which the data D_(PT) is produced exclusively in response to specific user requests, so-called single-point fixes; a third low-power mode in which only accurate time is needed (i.e. the data D_(PT) only includes time, and therefore it is sufficient to track a single satellite, and perhaps receive signals only once every tens of minutes to hours depending on the quality of the receiver's oscillator); and a fourth mode in which the data D_(PT) is updated at a user-specified rate, which can be anything from the rate of the first mode to the rate of the third mode. Naturally, for a given data quality, each of these modes has an average processing demand per unit time that typically is different from the other modes. Therefore, if the future operational mode is expected to be different from the current operational mode (for example resulting from a programmed mode switch of the receiver behavior, or a user command), the processing unit 135 is preferably adapted to estimate a respective processing demand for producing the position/time related data D_(PT) in the future operational mode instead of the current operational mode. In this case, the estimation of the anticipated processing intensity is still based on each subset of a number of candidate subsets of signal sources among the group of potential signal sources, such as S1-S9 above. Moreover, each candidate subset contains at least a minimum number of signal sources, which is required to produce the position/time related data D_(PT) in the future operational mode.

To sum up, we will now describe the general method of controlling a spread spectrum signal receiver according to the invention with reference to the flow diagram in FIG. 5.

An initial step 510 determines a candidate collection of subsets of signal sources among a group of potential signal sources. Each member of the collection is a subset of the group of potential signal sources, which is defined by a criterion that an anticipated processing intensity required to derive the position/time related data from that subset is equal to or less than a maximum specified processing intensity.

A subsequent step 520 selects a set of preferred signal sources. This set is the candidate subset, which is associated with the highest estimated quality of the position/time data without exceeding the maximum specified processing intensity. According to the invention, each of the candidate subsets contains at least a minimum number of signal sources necessary to produce the position/time related data. Therefore, in some operational modes, the number of candidate subsets is normally relatively large. However, determining the candidate subset that is associated with the highest estimated quality of the position/time related data during a subsequent operating period requires a comparatively small amount of processing resources. Hence, by optimizing the selection of signal sources, the overall usage of the processing unit's processing resources can be economized.

Then, a step 530 receives spread spectrum signals from the signal sources in the selected set. This reception continues during said operating period. Depending on the implementation and the receiver's current operational mode, the length of this period may vary from a few processor clock cycles to several minutes. Moreover, it is worth noting that the above-mentioned lowest estimated processing demand for producing the position/time data of a desired quality refers to the expected total amount of processing necessary to perform during said operating period. Thus, an initial processing peak may be acceptable provided that this peak is comparatively short, and the expected total amount of processing becomes sufficiently low during subsequent operations, so that for instance a user-specified average processing intensity requirement is met. Of course, according to the invention, an instantaneous (or peak) processing intensity may also be specified by the user.

All of the steps, as well as any sub-sequence of steps, described with reference to FIG. 5, above may be controlled by means of a programmed computer apparatus. Moreover, although the embodiments of the invention described above with reference to the drawings comprise computer apparatus and processes performed in computer apparatus, the invention thus also extends to computer programs, particularly computer programs on or in a carrier, adapted for putting the invention into practice. The program may be in the form of source code, object code, a code intermediate source and object code such as in partially compiled form, or in any other form suitable for use in the implementation of the procedure according to the invention. The program may either be a part of an operating system, or be a separate application. The carrier may be any entity or device capable of carrying the program. For example, the carrier may comprise a storage medium, such as a Flash memory, a ROM (Read Only Memory), for example a DVD (Digital Video/Versatile Disk), a CD (Compact Disc), an EPROM (Erasable Programmable Read-Only Memory), an EEPROM (Electrically Erasable Programmable Read-Only Memory), or a magnetic recording medium, for example a floppy disc or hard disc. Further, the carrier may be a transmissible carrier such as an electrical or optical signal which may be conveyed via electrical or optical cable or by radio or by other means. When the program is embodied in a signal which may be conveyed directly by a cable or other device or means, the carrier may be constituted by such cable or device or means. Alternatively, the carrier may be an integrated circuit in which the program is embedded, the integrated circuit being adapted for performing, or for use in the performance of, the relevant procedures.

The term “comprises/comprising” when used in this specification is taken to specify the presence of stated features, integers, steps or components. However, the term does not preclude the presence or addition of one or more additional features, integers, steps or components or groups thereof.

The reference to any prior art in this specification is not, and should not be taken as, an acknowledgement or any suggestion that the referenced prior art forms part of the common general knowledge in Australia.

The invention is not restricted to the described embodiments in the figures, but may be varied freely within the scope of the claims. 

1. A spread spectrum signal receiver comprising: a radio signal processing unit at least partly implemented in software running on a processor, the processing unit being adapted to select a set of preferred signal sources from a group of potential signal sources, receive spread spectrum signals from the selected set of signal sources, and based on the received signals produce position/time related data, wherein the processing unit is adapted to: determine a candidate collection of subsets of signal sources among a group of potential signal sources, each member of the collection being a subset of the group of potential signal sources defined by a criterion that an anticipated processing intensity required to derive the position/time related data from that subset is equal to or less than a maximum specified processing intensity; and select the set of preferred signal sources based on a candidate subset being associated with a highest estimated quality of the position/time related data attainable during a subsequent operating period for the receiver without exceeding the maximum specified processing intensity.
 2. The receiver according to claim 1, wherein at least two of the signal sources in the group of potential signal sources emit signals of mutually different signal formats, and the processing unit is adapted to estimate the anticipated processing intensity by considering a respective computational complexity for processing signals of each of said signal formats.
 3. The receiver according to claim 1, wherein the processing unit is adapted to estimate the anticipated processing intensity by considering a predetermined approximation of a processing intensity required to derive the position/time related data from a particular signal source based on at least one of: a format of the signals emitted from the source; or a parameter reflecting the source's elevation angle relative to the receiver.
 4. The receiver according to claim 1, wherein the processing unit is adapted to adjust a respective processing intensity allocated to process the signals from each signal source in the set of preferred signal sources, the adjustment being performed without the adjusted allocation exceeding the maximum specified processing intensity.
 5. The receiver according to claim 1, wherein the processing unit is adapted to estimate the anticipated processing intensity by considering a respective signal quality of each signal in the group of potential signal sources, the signal quality being reflected by at least one of: a signal power parameter estimation; a noise density parameter estimation; a pseudorange error parameter estimation; a parameter indicating interference detection; or signal source health/status data.
 6. The receiver according to claim 1, wherein the processing unit is adapted to estimate the anticipated processing intensity by considering at least one geometry parameter reflecting a spatial position of each of the signal sources in the group of potential signal sources relative to a current position/time for the receiver.
 7. The receiver according to claim 1, wherein the processing unit is adapted to estimate the anticipated processing intensity by considering whether a future operational mode of the receiver during the subsequent operating period is expected to be identical to a current operational mode of the receiver.
 8. The receiver according to claim 7, wherein if the future operational mode is different from the current operational mode, the processing unit is adapted to estimate a respective anticipated processing intensity required to derive the position/time related data from each of the subsets in the candidate collection of subsets in the future operational mode.
 9. The receiver according to claim 1, wherein each signal source is represented by a particular satellite of at least one global navigation satellite system, and the group of potential signal sources represents all satellites of the at least one global navigation satellite system being visible from a current position/time for the receiver.
 10. The receiver according to claim 1, wherein the processing unit is adapted to optimize a cost function expressing a processing intensity as a function of a data quality level with respect to the position/time related data in order to determine the candidate subset which is associated with the highest estimated quality of the position/time related data during the subsequent operating period.
 11. A method of operating a spread spectrum signal receiver, comprising a radio signal processing unit, the method comprising: selecting a set of preferred signal sources from a group of potential signal sources; receiving spread spectrum signals from the selected set of signal sources; and producing position/time related data based on the received signals, the method of producing position and time related data further comprising: determining a candidate collection of subsets of signal sources among a group of potential signal sources, each member of the collection being a subset of the group of potential signal sources defined by a criterion that an anticipated processing intensity required to derive the position/time related data from that subset is equal to or less than a maximum specified processing intensity; and selecting the set of preferred signal sources based on a candidate subset being associated with a highest estimated quality of the position/time related data attainable during a subsequent operating period for the receiver without exceeding the maximum specified processing intensity.
 12. The method according to claim 11, wherein at least two of the signal sources in the group of potential signal sources emit signals of mutually different signal formats, and wherein determining a candidate collection of subsets comprises: estimating the anticipated processing intensity by considering a respective computational complexity for processing signals of each of said signal formats.
 13. The method according to claim 11, wherein determining a candidate collection of subsets comprises: estimating the anticipated processing intensity by considering a predetermined approximation of a processing intensity required to derive the position/time related data from a particular signal source; and basing the estimation on at least one of: a format of the signals emitted from the source; or a parameter reflecting the source's elevation angle relative to the receiver.
 14. The method according to claim 11, wherein selecting the set of preferred signal sources based on a candidate subset comprises: adjusting a respective processing intensity allocated to process the signals from each signal source in the set of preferred signal sources, the adjustment being performed without the adjusted allocation exceeding the maximum specified processing intensity.
 15. The method according to claim 11, wherein determining a candidate collection of subsets comprises: estimating the anticipated processing intensity by considering a respective estimated signal quality of each signal in the group of potential signal sources, the signal quality being reflected by at least one of: a signal power parameter estimation; a noise density parameter estimation; a pseudorange error parameter estimation; a parameter indicating interference detection; or signal source health/status data.
 16. The method according to claim 11, wherein determining a candidate collection of subsets comprises: estimating the anticipated processing intensity by considering at least one geometry parameter reflecting a spatial position of each of the signal sources in the group of potential signal sources relative to a current position/time for the receiver.
 17. The method according to claim 11, wherein determining a candidate collection of subsets comprises: estimating the anticipated processing intensity by considering whether a future operational mode of the receiver during the subsequent operating period is expected to be identical to a current operational mode of the receiver.
 18. The method according to claim 17, wherein provided that the future operational mode is different from the current operational mode, determining a candidate collection of subsets comprises: estimating a respective anticipated processing intensity required to derive the position/time related data from each of the subsets in the candidate collection of subsets in the future operational mode.
 19. The method according to claim 11, wherein each signal source is represented by a particular satellite of at least one global navigation satellite system, and the group of potential signal sources representing all satellites of the at least one global navigation satellite system is visible from a current position/time for the receiver.
 20. The method according to claim 11, wherein determining a candidate collection of subsets comprises: optimizing a cost function expressing a processing intensity as a function of a data quality level with respect to the position/time related data to determine the candidate subset which is associated with the highest estimated quality of the position/time related data during the subsequent operating period.
 21. A computer program product directly loadable into the memory of a computer, comprising software for controlling the method of claim 11 when said program is run on the computer.
 22. A computer readable medium, having a program recorded thereon, where the program is to make a computer control the method of claim
 11. 